# Quiz study guides

Author

Katie Schuler

Published

September 17, 2023

You will receive a single score (0-4, see rubric) for each topic area representing your understanding of the course material in that area. A great way to study for quizzes in general is to (1) study the lecture notes and (2) quiz yourself with the labs.

## 1 Quiz 1

Quiz 1 will test the following learning objectives, divided into 6 topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

1. R Basics: general
• Assign an object to a valid variable name, list all variables in the environment and remove them
• Get help with a function or package from R
• Return information about an object, including its structure, data type, length, and attributes
• Explain what functions and control flow are; differentiate between types of control flow
2. R Basics: vectors, operations, and subsetting
• Distinguish between an atomic vector and a list
• Create atomic vectors and determine their data types
• Differentiate between implicit and explicit coercion and coerce an object to another type
• Use arithmetic, comparison, and logical operators on vectors
• Explain how more complex data structures are built from atomic vectors and create them
• Distinguish between `NA` and `NULL`
• Subset vectors and higher dimensional objects with the `[`, `[[` and `\$` operators
3. Data importing
• Load the `tidyverse`, recognize the included packages, and critique code for redundant loading
• Construct a tidy dataset and critique whether a given dataset is tidy
• Use the map function from the `purr` package
• Create a tibble and distinguish between a tibble and a data frame
• Use `readr` to read delimited files and determine whether `readr` can read files of a given type
• Use `col_types` to add a column specifications and explain how readr guesses without it
• Solve the 3 most common importing problems we discussed in class
4. Data visualization: basics
• Describe how to create a plot with `ggplot2` including the 3 basic requirements
• Distinguish between mapping and setting aesthetics
• Describe how `ggplot2` maps categorical variables to aesthetics and interpret the 3 common warnings people encounter in this process
• Interpret `ggplot()` calls with explicit or implicit arguments for data and mapping
• Recognize the geoms we discussed in class and select which to use for a given situation
• Differentiate between globally and locally defined mappings and recognize them in given plot (or code)
5. Data visualization: layers
• Use the `position` argument to modify the position of the geoms in `geom_bar()` or `geom_point()`
• Describe `stat="identity"` and describe the default transformations for `geom_bar()`, `geom_histogram()`, and `geom_smooth()`
• Set the smoothing method for `geom_smooth()` and the bins or bindwidth for `geom_histogram()`
• Facet a plot with `facet_wrap()` and `facet_grid()`
• Modify axis, legend, and plot labels with `labs()`
• Apply a given theme to a plot and adjust the base font size or family.
• Describe scales and recognize the outcome of adding a scale layer
6. Data wrangling
• Describe the common structure of `dplyr` functions (aka verbs)
• Combine `dplyr` functions with the pipe operator to solve complex problems
• Manipulate rows with `filter()`, `arrange()`, and `distinct()`
• Maniuplate columns with `mutate()`, `select()`, and `rename()`
• Group and summarise data with `group_by()`, `summarise()`, and `ungroup()`
• Evaulate `dplyr` functions that include the common arguments we covered in class

## 2 Quiz 2

Quiz 2 will test the following learning objectives, divided into 4 topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

More learning objectives will be added as we cover these topics in lecture.

1. Sampling distribution

• Explore a simple dataset with a histogram and summary statistics
• Recognize uniform and Gaussian probability distributions in a plot or equation and use R’s functions `d*()`, `p*()`, and `r*()` to work with these distributions
• Explain the difference between the parameter and the paramter estimate
• Construct the sampling distribution of a paramater estimate with `infer` and quantify the spread of the distribution with a confidence interval.
2. Hypothesis testing

3. Model specification

• Classify a model as supervised or unsupervised, regression or classification, and linear or nonlinear
• Recognize the 4 ways of writing the linear model equation
4. Model fitting

• Estimate the free parameters of a linear model with the gradient descent approach
• Estimate the free parameters of a linear model with the OLS approach